Modèle de régression avec variables d’écart
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A look at M. G. Dagenais' contributions (1969, 1973) on threshold regression models and at chapter 9 of S.M. Goldfeld and R.E. Quandt's book (1972) concerning switching regression models suggested to me that a new approach to estimating the threshold model by introducing slack variables might be possible. One of the main advantages of this new method is to simplify to a great extent the estimation of the likelihood function which is reduced partly to the problem of estimating a limited number of simple integrals for each iteration in the process of optimization. In order to facilitate a better understanding of our approach, two main models will be reviewed in the next section: the twin linear probability model (which can be estimated either by OLS, by a combination of probit and OLS, or by the tobit approach) and the threshold model. A critical look at the empirical results obtained by Dagenais (1973) will also be made before closing this section. Our new threshold model with slack variables is presented in section 3 and the main features of our new approach are summarized in the last section of this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it